Compressed Sensing Multiscale Sample Entropy Feature Extraction Method for Underwater Target Radiation Noise

Lei, Zhufeng; Lei, Xiaofang; Zhou, Chuanghui; Qing, Lyujun; Zhang, Qingyang

doi:10.1109/access.2022.3193129

Cited by 9 publications

(3 citation statements)

References 11 publications

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“…However, the mechanism of radiated noise generated by underwater targets is very complex, and the radiated noise contains multiple components, including continuous spectrum components and strong discrete spectrums. Furthermore, many factor such as spatiotemporal variations in the underwater acoustic channel, multipath effects, and Doppler effects can all affect the propagation of underwater acoustic signals [ 3 , 4 , 5 ]. As a result, UATR is a highly challenging and difficult technology.…”

Section: Introductionmentioning

confidence: 99%

A Novel Underwater Acoustic Target Recognition Method Based on MFCC and RACNN

Liu,

Yang,

Hou

et al. 2024

Sensors

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In ocean remote sensing missions, recognizing an underwater acoustic target is a crucial technology for conducting marine biological surveys, ocean explorations, and other scientific activities that take place in water. The complex acoustic propagation characteristics present significant challenges for the recognition of underwater acoustic targets (UATR). Methods such as extracting the DEMON spectrum of a signal and inputting it into an artificial neural network for recognition, and fusing the multidimensional features of a signal for recognition, have been proposed. However, there is still room for improvement in terms of noise immunity, improved computational performance, and reduced reliance on specialized knowledge. In this article, we propose the Residual Attentional Convolutional Neural Network (RACNN), a convolutional neural network that quickly and accurately recognize the type of ship-radiated noise. This network is capable of extracting internal features of Mel Frequency Cepstral Coefficients (MFCC) of the underwater ship-radiated noise. Experimental results demonstrate that the proposed model achieves an overall accuracy of 99.34% on the ShipsEar dataset, surpassing conventional recognition methods and other deep learning models.

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Section: Introductionmentioning

confidence: 99%

A Novel Underwater Acoustic Target Recognition Method Based on MFCC and RACNN

Liu,

Yang,

Hou

et al. 2024

Sensors

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“…Considering the complexity of the underwater environment, we need to develop a target detection algorithm suitable for underwater equipment which requires a high precision and low computation as its advantages [9][10][11]. The YOLO series target detection algorithm is known for achieving a good balance between detection accuracy and speed [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

DSW-YOLOv8n: A New Underwater Target Detection Algorithm Based on Improved YOLOv8n

Liu,

Huang,

Duan

et al. 2023

Electronics

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Underwater target detection is widely used in various applications such as underwater search and rescue, underwater environment monitoring, and marine resource surveying. However, the complex underwater environment, including factors such as light changes and background noise, poses a significant challenge to target detection. We propose an improved underwater target detection algorithm based on YOLOv8n to overcome these problems. Our algorithm focuses on three aspects. Firstly, we replace the original C2f module with Deformable Convnets v2 to enhance the adaptive ability of the target region in the convolution check feature map and extract the target region’s features more accurately. Secondly, we introduce SimAm, a non-parametric attention mechanism, which can deduce and assign three-dimensional attention weights without adding network parameters. Lastly, we optimize the loss function by replacing the CIoU loss function with the Wise-IoU loss function. We named our new algorithm DSW-YOLOv8n, which is an acronym of Deformable Convnets v2, SimAm, and Wise-IoU of the improved YOLOv8n(DSW-YOLOv8n). To conduct our experiments, we created our own dataset of underwater target detection for experimentation. Meanwhile, we also utilized the Pascal VOC dataset to evaluate our approach. The mAP@0.5 and mAP@0.5:0.95 of the original YOLOv8n algorithm on underwater target detection were 88.6% and 51.8%, respectively, and the DSW-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 can reach 91.8% and 55.9%. The original YOLOv8n algorithm was 62.2% and 45.9% mAP@0.5 and mAP@0.5:0.95 on the Pascal VOC dataset, respectively. The DSW-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 were 65.7% and 48.3%, respectively. The number of parameters of the model is reduced by about 6%. The above experimental results prove the effectiveness of our method.

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Section: Introductionmentioning

confidence: 99%

DSW-YOLOv8n: A New Underwater Target Detection Algorithm Based on Improved YOLOv8n

Liu,

Huang,

Duan

et al. 2023

Preprint

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Underwater target detection is widely used in various applications such as underwater search and rescue, underwater environment monitoring, and Marine resources survey. However, the visibility of the underwater environment and the accuracy of target detection can be affected by complex underwater light changes and unpredictable background noise. To address these issues, we propose an improved underwater target detection algorithm based on YOLOv8n. Our algorithm focuses on three aspects. Firstly, we replace the original C2f module with Deformable Convnets v2 to enhance the adaptive ability of the target region in the convolution check feature map and extract the target region's features more accurately. Secondly, we introduce SimAm, a non-parametric attention mechanism, which can deduce and assign three-dimensional attention weights without adding network parameters. Lastly, we optimize the loss function by replacing the CIOU loss function with the Wise-IOU loss function. To conduct our experiments, we create our own dataset of underwater target detection for experimentation. Meanwhile, we also utilized the Pascal VOC dataset to evaluate our approach. The mAP@0.5 and mAP@0.5:0.95 of the original YOLOv8n algorithm on the underwater target detection were 88.6% and 51.8%, respectively, and the improved algorithm mAP@0.5 and mAP@0.5:0.95 can reach 91.8% and 55.9%. The original YOLOV8n algorithm was 62.2% and 45.9% mAP@0.5 and mAP@0.5:0.95 on the Pascal VOC dataset, respectively. The improved YOLOV8n algorithm mAP@0.5 and mAP@0.5:0.95 were 65.7% and 48.3%, respectively. The floating-point computation volume of the model is reduced by about 6%. The above experimental results prove the effectiveness of our method.

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Compressed Sensing Multiscale Sample Entropy Feature Extraction Method for Underwater Target Radiation Noise

Cited by 9 publications

References 11 publications

A Novel Underwater Acoustic Target Recognition Method Based on MFCC and RACNN

A Novel Underwater Acoustic Target Recognition Method Based on MFCC and RACNN

DSW-YOLOv8n: A New Underwater Target Detection Algorithm Based on Improved YOLOv8n

DSW-YOLOv8n: A New Underwater Target Detection Algorithm Based on Improved YOLOv8n

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